Users in a social network are usually confronted with decision making under uncertain network state. While there are some works in the social learning literature on how to construct belief on an uncertain network state, few study has been made on integrating learning with decision making for the scenario where users are uncertain about the network state and their decisions influence with each other. Moreover, the population in a social network can be dynamic since users may arrive at or leave the network at any time, which makes the problem even more challenging. In this paper, we propose a Dynamic Chinese Restaurant Game to study how a user in a dynamic social network learns the uncertain network state and make optimal decision by taking into account not only the immediate utility but also subsequent users’ negative influence. We introduce a Bayesian learning based method for users to learn the network state, and propose a Multi-dimensional Markov Decision Process based approach for users to achieve the optimal decisions. Finally, we apply the Dynamic Chinese Restaurant Game to cognitive radio networks and demonstrate from simulations to verify the effectiveness and efficiency of the proposed scheme.